code: def create_joined_df(gdf, everdf, **kwargs):
      chronometer = Chronometer.makeStarted()
      tables = {"perf": gdf, "everdf": everdf}
  
      query = """SELECT perf.loan_id as loan_id,
                  perf.monthly_reporting_period as mrp_timestamp,
                  EXTRACT(MONTH FROM perf.monthly_reporting_period)
                      as timestamp_month,
                  EXTRACT(YEAR FROM perf.monthly_reporting_period)
                      as timestamp_year,
                  COALESCE(perf.current_loan_delinquency_status, -1)
                      as delinquency_12,
                  COALESCE(perf.current_actual_upb, 999999999.9) as upb_12,
                  everdf.ever_30 as ever_30,
                  everdf.ever_90 as ever_90,
                  everdf.ever_180 as ever_180,
                  COALESCE(everdf.delinquency_30, DATE '1970-01-01')
                      as delinquency_30,
                  COALESCE(everdf.delinquency_90, DATE '1970-01-01')
                      as delinquency_90,
                  COALESCE(everdf.delinquency_180, DATE '1970-01-01')
                      as delinquency_180
                  FROM main.perf as perf
                  LEFT OUTER JOIN main.everdf as everdf
                  ON perf.loan_id = everdf.loan_id"""
  
      results = pyblazing.run_query(query, tables)
      Chronometer.show(chronometer, "Create Joined DF")
      return results
  
imports: import time
  import pandas
  import pyblazing
  from pyblazing import DriverType, FileSystemType
  from sklearn.model_selection import train_test_split
  import xgboost
  from Configuration import Settings
  from DataBase import createSchema
  from DemoTest.chronometer import Chronometer
  from Utils import Execution
label_desc: Use image node_id 11 for calling a global function with example usage: create_joined_df(gdf, everdf) and returns: results